with Michael McAuliffe, Rajhansa Sridhara, Michael Hurley and Kristin Sainani
This course aims to provide a firm grounding in the foundations of probability and statistics. Specific topics include:
1. Describing data (types of data, data visualization, descriptive statistics) 2. Statistical inference (probability, probability distributions, sampling theory, hypothesis testing, confidence intervals, pitfalls of p-values) 3. Specific statistical tests (ttest, ANOVA, linear correlation, non-parametric tests, relative risks, Chi-square test, exact tests, linear regression, logistic regression, survival analysis; how to choose the right statistical test)
The course focuses on real examples from the medical literature and popular press. Each week starts with "teasers," such as: Should I be worried about lead in lipstick? Should I play the lottery when the jackpot reaches half-a-billion dollars? Does eating red meat increase my risk of being in a traffic accident? We will work our way back from the news coverage to the original study and then to the underlying data. In the process, participants will learn how to read, interpret, and critically evaluate the statistics in medical studies.
The course also prepares participants to be able to analyze their own data, guiding them on how to choose the correct statistical test and how to avoid common statistical pitfalls. Optional modules cover advanced math topics and basic data analysis in R.
There are no prerequisites for this course.
Participants will need to be familiar with a few basic math tools: summation sign, factorial, natural log, exponential, and the equation of a line; a brief tutorial is available on the course website for participants who need a refresher on these topics.
Week 1 - Descriptive statistics and looking at data Week 2 - Review of study designs; measures of disease risk and association Week 3 - Probability, Bayes' Rule, Diagnostic Testing Week 4 - Probability distributions Week 5 - Statistical inference (confidence intervals and hypothesis testing) Week 6 - P-value pitfalls; types I and type II error; statistical power; overview of statistical tests Week 7 - Tests for comparing groups (unadjusted); introduction to survival analysis Week 8 - Regression analysis; linear correlation and regression Week 9 - Logistic regression and Cox regression
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.